Power load prediction and energy-saving control device based on artificial intelligence

Abstract

The invention belongs to the technical field of power grids, and provides an artificial intelligence-based power load prediction and energy-saving control device, which comprises a data acquisition module used for acquiring operation data of a power system in real time, a data preprocessing module used for performing cleaning, normalization processing and feature extraction on the acquired data, and a power load prediction module used for performing power load prediction and energy-saving control; key features related to power load changes are extracted; the artificial intelligence prediction model is used for predicting the change trend of the power load according to the preprocessed data; the energy-saving control strategy module is used for formulating an energy-saving control strategy according to the prediction result and generating a control instruction; the execution module is used for adjusting equipment of the power system in real time according to the control instruction to realize energy-saving optimization control; the operation parameters of the power system are dynamically adjusted according to the prediction result, energy-saving optimization control is achieved, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids, and specifically relates to a power load forecasting and energy-saving control device based on artificial intelligence. Background Art

[0002] With the rapid development of social economy, the demand for power load is increasing continuously, and the stable operation of the power system and the energy utilization efficiency are facing huge challenges. Traditional power load forecasting methods mainly rely on historical data and empirical formulas, with low forecasting accuracy and difficulty in adapting to complex and variable power load changes.

[0003] Therefore, those skilled in the art have proposed a power load forecasting and energy-saving control device based on artificial intelligence to solve the problems raised in the background art. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a power load forecasting and energy-saving control device based on artificial intelligence to solve the problem that traditional power load forecasting methods in the prior art mainly rely on historical data and empirical formulas, with low forecasting accuracy and difficulty in adapting to complex and variable power load changes.

[0005] A power load forecasting and energy-saving control device based on artificial intelligence includes:

[0006] A data acquisition module for real-time acquisition of the operation data of the power system, including information such as voltage, current, power, load change rate, ambient temperature and humidity;

[0007] A data preprocessing module for cleaning, normalizing and feature extraction of the acquired data, and extracting key features related to power load changes;

[0008] An artificial intelligence forecasting model for predicting the change trend of power load according to the preprocessed data;

[0009] An energy-saving control strategy module for formulating an energy-saving control strategy according to the prediction result and generating a control instruction;

[0010] An execution module for real-time adjustment of the equipment of the power system according to the control instruction to achieve energy-saving optimization control;

[0011] A monitoring and feedback module for real-time monitoring of the operation status and energy-saving effect of the power system, and transmitting the feedback information to the data acquisition module and the energy-saving control strategy module to achieve closed-loop control.

[0012] Preferably, the artificial intelligence forecasting model is constructed based on a deep learning algorithm, specifically a long short-term memory network, a convolutional neural network or a hybrid neural network model.

[0013] Preferably, the energy-saving control strategy module adopts an optimization algorithm, specifically a genetic algorithm, a particle swarm optimization algorithm, or a simulated annealing algorithm, to dynamically adjust the operating parameters of the power system to achieve energy-saving optimization control.

[0014] Preferably, the execution module is used to adjust the tap position of the transformer in the power system, the switching of reactive power compensation devices, and the output of the generator set to achieve energy-saving optimization control.

[0015] Preferably, the monitoring and feedback module is used to monitor the operating status and energy-saving effect of the power system in real time, and transmit the feedback information to the data acquisition module and the energy-saving control strategy module to achieve closed-loop control.

[0016] Preferably, the data preprocessing module includes a data cleaning unit, a normalization processing unit, and a feature extraction unit to improve the quality and usability of the data.

[0017] Preferably, a data storage module is further included to store the collected operating data, the preprocessed data, the prediction results, and the energy-saving control strategy for subsequent analysis and optimization.

[0018] Preferably, the data acquisition module realizes the real-time acquisition of the operating data of the power system through a sensor network or an intelligent meter, and transmits the data to the data preprocessing module through wireless or wired communication.

[0019] Preferably, a user interaction module is further included to display the prediction results, the energy-saving control strategy, and the operating status information, and allow the user to manually adjust the operating parameters of the device.

[0020] Preferably, a security module is further included to encrypt the collected and transmitted data, and conduct security monitoring on the operation of the device to prevent data leakage and malicious attacks.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. By introducing artificial intelligence technology, the present invention can more accurately predict the change trend of power load, providing a scientific basis for the operation and dispatching of the power system.

[0023] 2. By dynamically adjusting the operating parameters of the power system according to the prediction results, the present invention realizes energy-saving optimization control and improves energy utilization efficiency.

[0024] 3. Through the closed-loop control mechanism, the present invention monitors and adjusts the operating status of the power system in real time, enhancing the stability and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1It is an overall schematic diagram of an artificial intelligence-based power load prediction and energy-saving control device. Specific Embodiment

[0026] The following further describes the embodiments of the present invention in detail in conjunction with the drawings and examples. The following examples are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0027] Embodiment 1

[0028] As shown in the Figure 1 accompanying drawings, the present invention provides an artificial intelligence-based power load prediction and energy-saving control device, including

[0029] a data acquisition module, which is used to collect the operation data of the power system in real time, including information such as voltage, current, power, load change rate, environmental temperature and humidity;

[0030] a data preprocessing module, which is used to clean, normalize and extract features from the collected data, and extract key features related to the change of power load;

[0031] an artificial intelligence prediction model, which is used to predict the change trend of power load according to the preprocessed data;

[0032] an energy-saving control strategy module, which is used to formulate an energy-saving control strategy according to the prediction result and generate a control instruction;

[0033] an execution module, which is used to adjust the equipment of the power system in real time according to the control instruction to achieve energy-saving optimization control;

[0034] a monitoring and feedback module, which is used to monitor the operation state and energy-saving effect of the power system in real time, and transmit the feedback information to the data acquisition module and the energy-saving control strategy module to achieve closed-loop control.

[0035] As can be seen from the above, install a data acquisition module at the key nodes of the power grid system to collect operation data such as voltage, current, and power in real time; transmit the collected data to the data preprocessing module for cleaning, normalization and feature extraction; build a power load prediction model and train and optimize the model using historical data. Predict the change trend of power load within the next 24 hours through the model; according to the prediction result, combined with the operation state and constraints of the power grid system, use the genetic algorithm to formulate an energy-saving control strategy, such as adjusting the tap position of the transformer and optimizing the output of the generator set; the execution module adjusts the equipment of the power grid system in real time according to the control instruction to achieve energy-saving optimization control; the monitoring and feedback module monitors the operation state and energy-saving effect of the power grid system in real time, and transmits the feedback information to the data acquisition module and the energy-saving control strategy module to achieve closed-loop control.

[0036] Embodiment 2

[0037] As shown in the Figure 1 accompanying figure, this embodiment is basically the same as the previous one, except that the artificial intelligence prediction model is constructed based on deep learning algorithms, specifically long short-term memory network, convolutional neural network or hybrid neural network model, which can effectively capture the spatio-temporal features and long-term dependencies in the power load data, and significantly improve the prediction accuracy.

[0038] Specifically, the energy-saving control strategy module adopts optimization algorithms, specifically genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm, to dynamically adjust the operating parameters of the power system to achieve energy-saving optimization control. Through dynamic optimization control, the system losses are significantly reduced, the operating costs are decreased, and the energy utilization efficiency is improved.

[0039] Furthermore, the execution module is used to adjust the tap position of the transformer in the power system, the switching of the reactive power compensation device, and the output of the generator set to achieve energy-saving optimization control.

[0040] Furthermore, the monitoring and feedback module is used to monitor the operating state and energy-saving effect of the power system in real time, and transmit the feedback information to the data acquisition module and the energy-saving control strategy module to achieve closed-loop control. The execution module adjusts the equipment of the power system in real time according to the instructions generated by the energy-saving control strategy module, reducing unnecessary energy waste.

[0041] Furthermore, the data preprocessing module includes a data cleaning unit, a normalization processing unit and a feature extraction unit, which are used to improve the quality and usability of the data. Efficient data preprocessing can significantly improve the convergence speed and prediction accuracy of the model.

[0042] Embodiment III

[0043] As shown in the Figure 1 accompanying figure, this embodiment is basically the same as the previous one, except that it further includes a data storage module, which is used to store the collected operating data, preprocessed data, prediction results and energy-saving control strategies for subsequent analysis and optimization. Through efficient data storage and management, it supports the long-term preservation and rapid retrieval of large-scale data, providing a basis for the continuous optimization of the system.

[0044] Specifically, the data acquisition module realizes the real-time acquisition of the operating data of the power system through a sensor network or smart meters, and transmits the data to the data preprocessing module through wireless or wired communication methods. Real-time data acquisition improves the response speed and prediction accuracy of the system, supporting dynamic optimization.

[0045] Furthermore, it further includes a user interaction module, which is used to display the prediction results, energy-saving control strategies, and operating status information, and allows users to manually adjust the operating parameters of the device. By providing a flexible operation interface, it meets the needs of different users and enhances the adaptability and user experience of the system.

[0046] Furthermore, it further includes a security module, which is used to encrypt the collected and transmitted data and conduct security monitoring on the operation of the device to prevent data leakage and malicious attacks. Through encryption and access control mechanisms, it ensures the security of the system and prevents data tampering and unauthorized access.

[0047] The standard parts used in the present invention can all be purchased from the market. The special-shaped parts can be customized according to the descriptions in the specification and the drawings. The specific connection methods of each part all adopt conventional means such as bolts, rivets, and welding that are mature in the prior art. The machines, parts, and equipment all adopt conventional models in the prior art. Coupled with the circuit connection adopting the conventional connection method in the prior art, details are not described herein again. The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0048] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "a plurality" is two or more unless otherwise specifically defined.

[0049] In the present invention, unless otherwise clearly defined and limited, the terms such as "installed", "connected", "connected to", and "fixed" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] In the present invention, unless otherwise clearly defined and limited, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over", and "on" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "below", and "beneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0051] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0052] In the drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved, and other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0053] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An artificial intelligence-based power load forecasting and energy-saving control device, characterized in that: include, Data acquisition module, used to collect real-time operation data of the power system, including voltage, current, power, load change rate, ambient temperature and humidity, etc.; The data preprocessing module is used to clean, normalize and extract features of the collected data, and extract key features related to power load changes; Artificial intelligence prediction model, used to predict the changing trend of power load based on preprocessed data; Energy-saving control strategy module, used to formulate energy-saving control strategy according to prediction results and generate control instructions; The execution module is used to adjust the equipment of the power system in real time according to the control instructions to achieve energy-saving optimization control; The monitoring and feedback module is used to monitor the operating status and energy-saving effect of the power system in real time, and transmit the feedback information to the data acquisition module and the energy-saving control strategy module to achieve closed-loop control.

2. The power load forecasting and energy-saving control device based on artificial intelligence as claimed in claim 1, characterized in that: The artificial intelligence prediction model is built based on a deep learning algorithm, specifically a long short-term memory network, a convolutional neural network or a hybrid neural network model.

3. The power load forecasting and energy-saving control device based on artificial intelligence as claimed in claim 2, characterized in that: The energy-saving control strategy module adopts an optimization algorithm, specifically a genetic algorithm, a particle swarm optimization algorithm or a simulated annealing algorithm, for dynamically adjusting the operating parameters of the power system to achieve energy-saving optimization control.

4. The power load forecasting and energy-saving control device based on artificial intelligence as claimed in claim 3, characterized in that: The execution module is used to adjust the transformer tap position, the switching of the reactive power compensation device, and the output of the generator set of the power system to achieve energy-saving optimization control.

5. The power load forecasting and energy-saving control device based on artificial intelligence as claimed in claim 4, characterized in that: The monitoring and feedback module is used to monitor the operating status and energy-saving effect of the power system in real time, and transmit feedback information to the data acquisition module and the energy-saving control strategy module to achieve closed-loop control.

6. The power load forecasting and energy-saving control device based on artificial intelligence as claimed in claim 5, characterized in that: The data preprocessing module includes a data cleaning unit, a normalization processing unit and a feature extraction unit, which are used to improve the quality and availability of data.

7. The power load forecasting and energy-saving control device based on artificial intelligence as claimed in claim 6, characterized in that: It also includes a data storage module for storing collected operating data, pre-processed data, prediction results and energy-saving control strategies to facilitate subsequent analysis and optimization.

8. The power load forecasting and energy-saving control device based on artificial intelligence as claimed in claim 7, characterized in that: The data acquisition module realizes real-time acquisition of power system operation data through a sensor network or a smart meter, and transmits the data to the data preprocessing module through wireless or wired communication.

9. The power load forecasting and energy-saving control device based on artificial intelligence as claimed in claim 8, characterized in that: It also includes a user interaction module for displaying prediction results, energy-saving control strategies and operating status information, and allowing the user to manually adjust the operating parameters of the device.

10. The power load forecasting and energy-saving control device based on artificial intelligence as claimed in claim 9, characterized in that: It also includes a security module for encrypting the collected and transmitted data and performing security monitoring on the operation of the device to prevent data leakage and malicious attacks.

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